This paper presents a summary of the work done within the European Union's
Seventh Framework Programme project ECLIPSE (Evaluating the Climate and Air
Quality Impacts of Short-Lived Pollutants). ECLIPSE had a unique systematic
concept for designing a realistic and effective mitigation scenario for
short-lived climate pollutants (SLCPs; methane, aerosols and ozone, and
their precursor species) and quantifying its climate and air quality
impacts, and this paper presents the results in the context of this
overarching strategy. The first step in ECLIPSE was to create a new emission
inventory based on current legislation (CLE) for the recent past and until
2050. Substantial progress compared to previous work was made by including
previously unaccounted types of sources such as flaring of gas associated
with oil production, and wick lamps. These emission data were used for
present-day reference simulations with four advanced Earth system models
(ESMs) and six chemistry transport models (CTMs). The model simulations were
compared with a variety of ground-based and satellite observational data
sets from Asia, Europe and the Arctic. It was found that the models still
underestimate the measured seasonality of aerosols in the Arctic but to a
lesser extent than in previous studies. Problems likely related to the
emissions were identified for northern Russia and India, in particular. To
estimate the climate impacts of SLCPs, ECLIPSE followed two paths of
research: the first path calculated radiative forcing (RF) values for a
large matrix of SLCP species emissions, for different seasons and regions
independently. Based on these RF calculations, the Global Temperature change Potential metric for a time horizon of 20 years (GTP20) was calculated
for each SLCP emission type. This climate metric was then used in an
integrated assessment model to identify all emission mitigation measures
with a beneficial air quality and short-term (20-year) climate impact. These
measures together defined a SLCP mitigation (MIT) scenario. Compared to CLE,
the MIT scenario would reduce global methane (CH4) and black carbon (BC)
emissions by about 50 and 80 %, respectively. For CH4, measures
on shale gas production, waste management and coal mines were most
important. For non-CH4 SLCPs, elimination of high-emitting vehicles and
wick lamps, as well as reducing emissions from gas flaring, coal and biomass
stoves, agricultural waste, solvents and diesel engines were most important.
These measures lead to large reductions in calculated surface concentrations
of ozone and particulate matter. We estimate that in the EU, the loss of
statistical life expectancy due to air pollution was 7.5 months in 2010,
which will be reduced to 5.2 months by 2030 in the CLE scenario. The MIT
scenario would reduce this value by another 0.9 to 4.3 months.
Substantially larger reductions due to the mitigation are found for China
(1.8 months) and India (11–12 months). The climate metrics cannot fully
quantify the climate response. Therefore, a second research path was taken.
Transient climate ensemble simulations with the four ESMs were run for the CLE
and MIT scenarios, to determine the climate impacts of the mitigation. In
these simulations, the CLE scenario resulted in a surface temperature
increase of 0.70 ± 0.14 K between the years 2006 and 2050. For the
decade 2041–2050, the warming was reduced by 0.22 ± 0.07 K in the MIT
scenario, and this result was in almost exact agreement with the response
calculated based on the emission metrics (reduced warming of 0.22 ± 0.09 K). The metrics calculations suggest that non-CH4 SLCPs contribute
∼ 22 % to this response and CH4 78 %. This could not
be fully confirmed by the transient simulations, which attributed about
90 % of the temperature response to CH4 reductions. Attribution of
the observed temperature response to non-CH4 SLCP emission reductions
and BC specifically is hampered in the transient simulations
by small forcing and co-emitted species of the emission basket chosen.
Nevertheless, an important conclusion is that our mitigation basket as a
whole would lead to clear benefits for both air quality and climate. The
climate response from BC reductions in our study is smaller than reported
previously, possibly because our study is one of the first to use fully
coupled climate models, where unforced variability and sea ice responses
cause relatively strong temperature fluctuations that may counteract (and, thus, mask) the impacts of small emission reductions. The temperature
responses to the mitigation were generally stronger over the continents than
over the oceans, and with a warming reduction of 0.44 K (0.39–0.49) K the largest
over the Arctic. Our calculations suggest particularly beneficial climate
responses in southern Europe, where surface warming was reduced by about
0.3 K and precipitation rates were increased by about 15 (6–21) mm yr-1 (more
than 4 % of total precipitation) from spring to autumn. Thus, the
mitigation could help to alleviate expected future drought and water
shortages in the Mediterranean area. We also report other important results
of the ECLIPSE project.

Introduction

The United Nations Framework Convention on Climate Change (UNFCCC) requires
climate policies to “be cost-effective so as to ensure global benefits at
the lowest possible cost” and that “policies and measures should …be
comprehensive …[and] …cover all relevant sources,
sinks and reservoirs”. This was made operational by the Kyoto Protocol,
which sets limits on emissions of six different greenhouse gases (GHGs), or
groups of GHGs – carbon dioxide (CO2), CH4, nitrous
oxide (N2O), perfluorocarbons (PFCs), hydrofluorocarbons (HFCs) and
sulfur hexafluoride (SF6). Collectively these are often known as “the
Kyoto gases” or the “Kyoto basket”

Note that, formally,
only species given values of Global Warming Potentials (GWP) in IPCC's
Second Assessment Report were controlled during the first commitment period
(2008–2012) of the Kyoto Protocol. The second commitment period (2013–2020),
via the Doha Amendment, also includes NF3 in the list of greenhouse
gases, and uses GWP values from the IPCC's Fourth Assessment Report. The
Doha Amendment is currently not in force, as it awaits ratification by a
sufficient number of parties.

. CO2 is the most important anthropogenic
driver of global warming, with additional significant contributions from
CH4 and N2O. However, other anthropogenic emissions capable of
causing climate change are not covered by the Kyoto Protocol. Some are
covered by other protocols, e.g. emissions of chlorofluorocarbons (CFCs) and
hydrochlorofluorocarbons (HCFCs) are regulated by the Montreal Protocol,
because of their role in stratospheric ozone (O3) depletion. But there
are others, notably several short-lived components that give strong
contributions to climate change that are excluded from existing climate
agreements.

In the present study we investigate climate and air quality impacts of the
emissions of CH4, which has a lifetime of about 9 ± 1 years
(Prather et al., 2012) and a number of much shorter-lived components
(atmospheric lifetimes of months or less) which directly or indirectly (via
formation of other short-lived species) influence the climate (Myhre et al.,
2013a):

Methane is a greenhouse gas with a radiative efficiency (in W m-2 ppbv-1)
roughly 26 times greater than that of CO2 at current concentrations. It is relatively well-mixed
in the atmosphere and has both natural and anthropogenic sources. It is also a precursor of O3
and stratospheric water vapour.

Black carbon (BC, also commonly known as soot), a product of incomplete combustion of fossil
fuels and biomass, affects climate via several mechanisms (Bond et al., 2013). It causes warming
through absorption of sunlight and by reducing surface albedo when deposited on snow. BC also
affects clouds, with a consequent (but highly uncertain) impact on their distribution and radiative
properties (Boucher et al., 2013).

Tropospheric O3 is a greenhouse gas produced by chemical reactions from the emissions of
the precursors CH4, carbon monoxide (CO), non-CH4 volatile organic compounds (NMVOCs) and
nitrogen oxides (NOx). Emissions of these same precursors also impact on hydroxyl radical (OH)
concentrations with further impacts especially on CH4.

Several components have cooling effects on climate, mainly sulfate aerosol formed from sulfur
dioxide (SO2) and ammonia (NH3), nitrate aerosol formed from NOx and NH3, and organic
aerosol (OA) which can be directly emitted or formed from gas-to-particle conversion of NMVOCs. They cause
a direct cooling by scattering solar radiation and alter the radiative properties of clouds, very likely leading to further cooling.

We refer to these substances as short-lived climate pollutants (SLCPs) as
they also have detrimental impacts on air quality, directly or via formation
of secondary pollutants (Kirtman et al., 2013). Notice that we include the
precursors of O3 and secondary aerosols in our definition of SLCPs. We
also include CH4 in our study even though it is included in the Kyoto
Protocol, because of its relatively short lifetime compared to that of
CO2 and its importance for air quality via the formation of O3. We
do not include HFCs in our definition of SLCPs, as they have no significant
impact on air quality and can be regulated from a climate policy perspective
alone. For SLCPs, on the other hand, cost-effective environmental policy
measures should be designed such that they optimise both the climate and air
quality responses (Schmale et al., 2014). In some instances, control of the
emissions of a species is expected to reduce future warming and improve air
quality at the same time – a “win–win” situation (Anenberg et al., 2012);
in others, the control of emissions may be conflicting, in the sense that it
could increase warming while improving air quality (or vice versa) – in
this case, emission control involves a “trade-off” between the impacts.

The net climate impact since pre-industrial times of all short-lived
components other than CH4 together is very likely to be cooling due
primarily to sulfate aerosols (Myhre et al., 2013a). Whilst SLCP reductions
are clearly beneficial for air quality, elimination of all current
non-CH4 SLCP emissions would thus very likely lead to extra warming.
Nevertheless, targeted emission reductions of selected SLCPs which cause
warming (either directly or via formation of secondary species) have the
potential to reduce global warming on a short timescale, as well as
improving air quality. They may also reduce the rate of warming (Myhre et
al., 2011; Shindell et al., 2012) that is important, for example, for the
adaptation of ecosystems to climate change (as recognised by UNFCCC Art. 2)
and is expected to accelerate in the near future (Smith et al., 2015).
Reducing these selected SLCP emissions might be effective to help avoid (or
at least delay) certain undesired impacts of climate change (e.g. rapid sea
ice loss in the Arctic; Quinn et al., 2008). At least, optimised SLCP
emission reductions could help to reduce the undesired extra climate warming
caused by air quality policy measures that often do not consider climate
impacts.

For designing a successful SLCP emission abatement strategy, the key
CH4 sources are relatively straightforward to deal with because their
emission profile is dominated by CH4 (e.g. venting of natural gas,
rice paddies). Combustion sources, however, emit a mix of many
different SLCPs (e.g. BC, OA, NOx, SO2) as well as CO2. This
makes it difficult to reduce the emissions of warming agents (e.g. BC)
alone, as their control often also leads to removal of co-emitted cooling
agents (e.g. OA, SO2). To achieve a climate benefit, abatement
strategies will be most effective if they target sources with a high
fraction of warming species in their emissions (e.g. diesel vehicles)
(Unger et al., 2010).

Climate effects of SLCPs

There are several distinct issues that have to be addressed in considering
the impact of any proposed SLCP abatement strategy. First, there are large
uncertainties in estimates of the climate effects of SLCPs (see e.g. Myhre
et al., 2013a) and thus also in the effects of emission reductions. These
apply particularly to the impact of aerosols on cloud properties (e.g. Quaas
et al., 2009; Boucher et al., 2013), but there are also difficulties in
evaluating direct radiative effects of aerosols.

Second, the climate impact of short-lived components, even when averaged
globally, can depend strongly on location and time (e.g. summer vs. winter)
of emissions (Fuglestvedt et al., 1999; Wild et al., 2001; Berntsen et al.,
2005, 2006; Koch et al., 2007; Naik et al., 2005; Reddy and Boucher, 2007;
Shindell and Faluvegi, 2009). For well-mixed gases (e.g. Kyoto gases), a
single globally valid value of Global Warming Potentials (GWP; see Sect. 1.2 for more details) can be calculated for a chosen time horizon, and then
used to give the so-called “CO2-equivalent” emissions of a gas. By
contrast, for the non-CH4 SLCPs, the GWP depends significantly on when
and where the emission occurs. Not only does this complicate the calculation
of GWPs, but also it introduces an additional dimension into the framing of
climate policy. For instance, the importance of location for BC emissions
has received much attention in this context (Ramanathan and Carmichael,
2008; Shindell and Faluvegi, 2009).

Third, inhomogeneity in the climate response to radiative forcing (RF) is important for SLCPs.
The geographical pattern of RF due to the non-CH4 SLCPs is generally
concentrated close to the source of emission, and hence is quite distinct
from the global-scale forcing due to the Kyoto gases. The extent to which
these heterogeneous forcing patterns will trigger different climate
responses compared to well-mixed gases is an unresolved scientific issue,
even though the climate response generally occurs on larger spatial scales
(but mainly in the hemisphere where the forcing takes place; Joshi et al.,
2003; Shindell et al., 2010) than the forcing itself. One example of the
issue of inhomogeneity of response concerns the effects of absorption of
solar radiation by BC in the Arctic atmosphere. Flanner (2013) has shown
that in the Arctic BC located at low altitudes causes a strong local surface
warming, but BC located at higher altitudes causes a surface cooling, which
is due to the reduced solar radiation reaching the surface. Another
important example is emissions of NOx as these lead to a shorter-lived
(and hence more localised) positive RF due to increases in O3 and a
longer-lived (and hence more global) negative RF due to the increased rate
of destruction of CH4. This means that metrics based on global-mean
quantities may be poorly representative of the local impacts of an emission
as the response depends on both region and timescale (Shine et al., 2005;
Lund et al., 2012).

Fourth, SLCPs may have other effects on climate that go beyond global-mean
temperature (Andrews et al., 2010; Kvalevåg et al., 2013) such as
through changes in the hydrological cycle (Gedney et al., 2014) and in the
atmospheric circulation. For example, in south-east Europe there are
indications that changes in the radiation budget through direct and indirect
effects of aerosols have caused circulation, precipitation and evaporation
changes (Lelieveld et al., 2002; Tragou and Lascaratos, 2003). Thus, even a
cooling component may cause unwanted climate impacts (Shindell, 2015).

Taking the above points into account, the short lifetimes and regional
dependence of the climate impact of SLCP emissions make these species
fundamentally different to the long-lived GHGs regulated under the Kyoto
Protocol and these impacts and metric values are much more uncertain (Myhre
et al., 2013a). Furthermore, cooling aerosols may have partly compensated
the warming due to well-mixed greenhouse gases in the past, and this masking
effect must be considered when determining the sensitivity of the climate
system directly from observations (Knutti and Hegerl, 2008; Skeie et al.,
2014). This also reduces our ability to calculate future global warming
(e.g. Andreae et al., 2005; Meinshausen et al., 2009; Penner et al., 2010).
Thus, there is an urgent need to understand and quantify the role that these
components may play in international efforts to reduce global warming
(Jackson, 2009; Berntsen et al., 2010; Arneth et al., 2009; Rypdal et al.,
2009b; Molina et al., 2009; Unger et al., 2010).

Climate metrics to characterise the effect of SLCPs

The Kyoto Protocol to the UNFCCC is a multi-gas climate treaty that required
a method to place emissions of different gases on a common scale. It adopted
the GWP with a 100-year time horizon, GWP100, from the IPCC
(Intergovernmental Panel on Climate Change) Second Assessment Report as a
metric in order to derive so-called CO2-equivalents for non-CO2
gas emissions. The GWP has since then been widely used in implementing the
Kyoto Protocol, and for other purposes. However, it was not designed with a
particular climate policy in mind, and as a result, GWP may not be the best
choice for all particular policy objectives (e.g. Tanaka et al., 2009;
Fuglestvedt et al., 2010; Myhre et al., 2013a; Pierrehumbert, 2014).

The GWP gives the RF due to a pulse emission of a gas or aerosol, integrated
over some time horizon, relative to that of CO2. The choice of
time horizon has a significant impact on the metric value of an emission
(e.g. Skodvin and Fuglestvedt, 1997; Shine, 2009; Fuglestvedt et al., 2010;
Aamaas et al., 2013) and is a value-laden choice. The time-integrated nature
of the GWP means that it retains the memory of short-lived emissions even at
long-time horizons, when their forcing and most of the response have
subsided.

Several alternatives to the GWP have been proposed and of these, the Global Temperature change Potential (GTP) (Shine et al., 2005, 2007; Fuglestvedt et
al., 2010) has attracted most attention (e.g. Reisinger et al., 2010;
Boucher and Reddy, 2008; Gillett and Matthews, 2010; Collins et al., 2013).
The GTP gives the global-mean surface temperature change some time after a
pulse emission, relative to that of CO2. In contrast to the GWP, it
uses temperature as the indicator and is an “end point”, rather than an
“integrative”, metric. Therefore, it does not retain the memory of
short-lived emissions in the same way as the GWP. Difficulties with the GTP
include its dependence on the climate sensitivity and on the method of
incorporating the ocean's thermal response (Shine et al., 2007; Fuglestvedt
et al., 2010; Olivié and Peters, 2013).

The GTP may be more appropriate to target-based climate policies (UNEP/WMO,
2011) where the aim is to keep temperature change below some given limit,
such as the 2 ∘C limit in the UNFCCC's Copenhagen Accord. The choice of
time horizon is then no longer so arbitrary, but is linked to the time at
which, for example, 2 ∘C is likely to be reached. This use of the GTP
(Shine et al., 2007; Berntsen et al., 2010; Tanaka et al., 2013) mimics the
behaviour of more complex (but less transparent) metrics based on integrated
assessment models (Manne and Richels, 2001).

In its 5th Assessment Report, the IPCC assessed scientific aspects of
climate metrics and their applicability in policy making. It was emphasised
that the most appropriate metric and time horizon will depend on which
aspects of climate change are considered most important to a particular
application. The assessment also pointed out that there are limitations and
inconsistencies related to the treatment of indirect effects and feedbacks
(e.g. climate–carbon cycle feedbacks) in climate metrics. In this study, we
have adopted GTP20, the GTP over a 20-year time horizon, as our key
metric, after careful consideration of alternatives (see Sect. 3.4).

Air quality impacts of SLCPs

The impact of SLCPs on air quality occurs at both the local and regional
scale. While local emissions contribute to episodes of high pollution levels
which can cause acute health effects, the long-range transport of air
pollutants or their precursors even over intercontinental distances (e.g.
Stohl and Trickl, 1999; Dentener et al., 2010) can increase the background
concentrations upon which pollution episodes are superimposed. This is also
important because there is increasing evidence of harmful effects of
long-term exposure to particulate matter (PM), O3, deposited acidifying
compounds and nitrogen to human health and vegetation (Anenberg et al.,
2012). Thus, the impact of SLCPs on air quality is complex and requires
quantification on local to global scales. At an international level, these
aspects, including emission regulation, are covered by the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) and its protocols
including the Gothenburg Protocol and its amendments.

The International Agency for Research on Cancer classified outdoor air
pollution as carcinogenic to humans with sufficient evidence that it causes lung cancer. A positive association with an
increased risk of bladder cancer was also demonstrated. It has been
estimated that air pollution caused 223 000 deaths from lung cancer
worldwide in 2010 (Anenberg et al., 2012; Lim et al., 2012). Air quality
guidelines for various substances published by different agencies are listed
in Table 1.

Air quality standards for Europe (European Union reference values),
WHO air quality guidelines (AQG), US-EPA National Ambient Air Quality
Standards (NAAQS) and the Environmental Quality Standards (EQS) and guideline
values for air pollutants in Japan. Values in brackets give time period for
which the guideline is defined.

Ozone and PM are the most problematic air pollutants with regard to effects
on human health (EEA, 2013). Ozone can, through impairment of lung function,
lead to premature deaths and increased hospitalisation (West et al., 2006).
PM was classified as carcinogenic to humans (IARC, 2015; Grosse, 2013). It is estimated, for
instance, that an increase of 10 µg m-3 in the concentrations of
PM10 (PM with diameter smaller than 10 µm) will increase
cardiopulmonary mortality by 9 % (Pope III et al., 1995). Different aerosol
types are considered when assessing climate impacts, whereas air quality
legislation is based on the concept of total mass concentrations of
particulate matter – either as PM2.5 or PM10. It is, however,
likely that human health impacts also depend on PM composition. For
instance, according to the World Health Organization (WHO), epidemiological
evidence indicates an association of daily variation in BC concentrations
with short and long-term adverse health effects such as
cardiovascular mortality, and cardiopulmonary hospital admissions.
Additionally, BC was classified as possibly carcinogenic to humans (Group 2B) (WHO, 2012). However,
concentration-response functions for individual PM components still need to
be established. Thus, neither BC nor ultrafine particles are currently
covered specifically by EU guidelines (WHO, 2013).

Scope and overall concept

The purpose of this paper is to present a summary of the work done within
the European Union's Seventh Framework Programme project ECLIPSE (Evaluating
the Climate and Air Quality Impacts of Short-Lived Pollutants). ECLIPSE had
a unique systematic concept for designing a realistic and effective SLCP
mitigation scenario and quantifying its climate and air quality impacts,
which is schematically shown in Fig. 1. Other papers describe particular
aspects of the ECLIPSE work in more detail, while we here present key
ECLIPSE results in the context of this overarching strategy and overall
conclusions of the project.

Schematic of the ECLIPSE overall methodology. Numbers in brackets
correspond to section numbers in this paper.

The first step in ECLIPSE was to create a new set of global baseline
emissions for the recent past and future (see Sect. 3.1, top of Fig. 1).
These emission data were used for present-day reference simulations with
Earth system models (ESMs) and chemistry transport models (CTMs). The model
simulations were compared extensively with a variety of global ground-based
and satellite observational data sets, in particular in three target areas
(China, Europe and the Arctic; see Sect. 3.2) to evaluate their
capabilities to simulate SLCP concentrations.

To study the climate impacts of SLCPs, ECLIPSE followed two paths of
research. The first path (the outer part of the spiral in Fig. 1) calculated
RF values for a large matrix of SLCP species emissions, for different
seasons and regions independently by changing the emissions of one species
from one region and one season at a time (see Sect. 3.3). Based on these
RF calculations, suitable metrics were chosen to allow the estimation of the
climate impact of particular SLCP emissions over different time horizons
(see Sect. 3.4).

These metrics were then used to generate an SLCP mitigation scenario to
minimise climate impacts that could be contrasted with the current
legislation scenario (see the top of Fig. 1). For this, the region-, season-
and species-specific matrix of climate impact (as defined by the chosen
metric) was used as an input to an integrated assessment model. All region-,
season- and sector-specific emission mitigation measures with a beneficial
air quality impact were then evaluated according to their expected climate
benefit. Notice here that emission measures typically affect several SLCP
species. For every mitigation measure, the emission reduction of every SLCP
species was therefore weighted with the chosen climate metric and summed
over all emitted SLCP species. Finally, all measures with beneficial air
quality and climate impacts were collected in a basket defining the SLCP
mitigation scenario (see Sect. 3.5).

The metrics, however, cannot fully quantify the climate response, due to the
underlying simplifying assumptions, including linearity, the need to specify
particular time horizons and, most importantly, the focus on one single
aspect of climate change (global-mean temperature for the chosen GTP
metric). Therefore, a second research path (the inner part of the spiral in
Fig. 1) was taken to determine the climate response for a set of emission
reductions for individual SLCP species, using a small ensemble of four
advanced ESMs. Furthermore, transient climate ensemble
simulations with these ESMs were run for the baseline and emission
mitigation scenarios, to calculate the transient climate and air quality
impacts of the mitigation scenario (see Sect. 3.6). A comparison between
the climate impacts expected from the metrics and those calculated with the
transient simulations (left part of the spiral in Fig. 1) closed the loop
between the first and the second research path and allowed for the evaluation of
the consistency of both approaches (see Sect. 3.7).

In ECLIPSE, we used multi-model ensemble results wherever possible, as these
are more robust than results from an individual model. However, certain
calculations could only be performed by a single model and are, thus,
presented as such.

ResultsThe ECLIPSE emissions

The ECLIPSE emission data set was created with the GAINS (Greenhouse gas–Air pollution Interactions and Synergies; http://www.iiasa.ac.at/web/home/research/researchPrograms/GAINS.en.html) model
(Amann et al., 2011), which provides emissions of long-lived greenhouse
gases and shorter-lived species in a consistent framework. The GAINS model
holds essential information about key sources of emissions, environmental
policies and mitigation opportunities for about 160 country regions. The
model relies on exogenous projections of energy use, industrial production,
and agricultural activity (ECLIPSE scenarios draw on IEA, 2012, for energy
and Alexandros and Bruinsma, 2012, for agriculture) for which it
distinguishes all key emission sources and control measures. More than 2000
technologies to control air pollutant emissions and at least 500 options to
control GHG emissions are included.

Improvements in the emission model were made especially for China (Zhao et
al., 2013; Wang et al., 2014), where large changes have occurred recently,
as well as for Europe where results of the consultation process during the
development of scenarios for the review of the EU National Emission Ceilings
Directive (Amann and Wagner, 2014) were used. Furthermore, several sources
like brick making, oil and gas production, non-ferrous metals and
international shipping were reviewed and updated. Finally, a number of
previously unaccounted sources were added or specifically distinguished in
the model, e.g. wick lamps, diesel generators and high-emitting vehicles.
The global SO2 inventory used for IPCC's 5th Assessment Report (Klimont
et al., 2013) was also developed during ECLIPSE.

All emission data were gridded consistently to a resolution of
0.5∘× 0.5∘ longitude–latitude. The spatial proxies used
in GAINS for gridding are consistent with those applied within the IPCC's
Representative Concentration Pathways (RCPs) projections as described in
Lamarque et al. (2010) and as further developed within the Global Energy
Assessment project (GEA, 2012). They were, however, modified to accommodate
more recent year-specific information where available, e.g. on population
distribution, open biomass burning, location of oil and gas production, and
livestock-specific spatial production patterns (Klimont et al., 2013,
2015b). Emissions were also temporally allocated: monthly distribution was
provided for all sources and for the residential heating emissions were
based on ambient air temperature (see Stohl et al., 2013).

For the first time in a global emission inventory, emissions from flaring of
associated gas in oil production were considered directly, including spatial
distribution. For BC, these emissions constitute only about 3 % of the
global total. However, owing to emissions in Russia, they constitute about
one third of all BC emissions north of 60∘ N and two thirds of all
emissions north of 66∘ N. Stohl et al. (2013) found that the gas
flaring emissions contribute 42 % of all BC found in the Arctic near the
surface, and this has improved the performance of the ECLIPSE models in the
Arctic.

Global annual anthropogenic emissions of CO2, CH4 and
key air pollutants (SO2, NOx and BC) for the current legislation
(CLE), no further controls (NFC) and ECLIPSE SLCP mitigation scenario. Units
are Gt for CO2 and Mt for the SLCPs. Also shown for comparison is the
range of the RCP emission scenarios (grey shading).

Current legislation (CLE) includes current and planned environmental laws,
considering known delays and failures up to now but assuming full enforcement in the future.

No further control (NFC) uses the same assumptions as CLE until 2015 but no further
legislation is introduced subsequently, even if currently committed. This leads to higher
emissions than in CLE for most pollutants.

The ECLIPSE SLCP mitigation (MIT) scenario includes all measures with beneficial air
quality and climate impact (according to the climate metric; see Sect. 3.4 and 3.5).

Different versions of the ECLIPSE inventory (available on request from
http://eclipse.nilu.no; also available from http://www.iiasa.ac.at/web/home/research/researchPrograms/Global_emissions.html) have been developed and were available at different times
for different tasks (Klimont et al., 2015a, b). We describe here the version
5, which was used for the transient climate model simulations (Sect. 3.6).
For model evaluation (Sect. 3.2) and climate perturbation simulations in
Sect. 3.6, versions 4 and 4a were used that, for the CLE scenario, were
very similar to version 5 (Klimont et al., 2015a, b).

During the past few decades, there was strong growth in CO2
emissions, but the SLCP emissions have followed a different trajectory, at
least at the global level. For example, the SO2 emissions have been
decreasing since 1990, with a temporary increase between 2000 and 2005
(Klimont et al., 2013), owing to strong policies and drastic reductions in
Europe and North America. The strong development in Asia was offset at the
global level by these reductions but in the future, emissions of SO2
grow again in the CLE scenario, primarily due to a strong increase in India
(Klimont et al., 2013). In fact, also some other SLCPs (e.g. NOx) show
signs of a rebound around the years 2020–2025, when most of the existing
policies will have been fully introduced (Klimont et al., 2015a, b). This is
driven by increasing fossil fuel use and thus coupled to increasing CO2
emissions. In the case of BC, GAINS does not predict further growth in
emissions, mostly because current policies to reduce coal use in China for
cooking and heating seem to be effective and because of the introduced
diesel legislation.

The NFC scenario has higher SLCP emissions than the CLE scenario, showing
the importance of actual introduction of already planned policies. However,
the NFC scenario still might be optimistic as it actually does not assume
any failure or further delays in enforcement of pre-2015 laws. The MIT
scenario, which shows deep cuts in the emissions of some species, is the
result of a climate-optimised SLCP reduction scenario and is described in
Sect. 3.5.

Figure 2 indicates a large spread in possible future emission pathways,
which for the air pollutants is larger than anticipated in the RCP
scenarios, shown by the grey shading. RCP scenarios focused on building
future emission scenarios with different RF from
long-lived GHGs while for air pollutants all assumed a very similar path,
strongly linked with the economic growth (Amann et al., 2013). Consequently
all air pollutant emissions decline strongly towards 2050 in all RCP
scenarios. This is not the case for the ECLIPSE emissions, and the spread is
larger than the RCP spread despite the fact that all scenarios follow the
same energy use projection.

Emissions from international shipping differ between the ECLIPSE emission
versions 4 and 5. Version 4a still drew on the work done for the RCP
scenarios, while for the version 5 data set, the historical emissions rely on
the results of Endresen et al. (2007), with activity data projected with
growth rates from IEA (2012). This allowed us to model region-specific
regulation, i.e. specifically in the emission control areas, and long-term targets to reduce the sulfur content of fuels. For aviation, the
emissions originate from Lee at al. (2009) and are consistent with the RCP
scenarios.

Non-agricultural, open biomass burning emissions are not calculated in the
GAINS model and, for the model simulations, were therefore taken from the
Global Fire Emission Database (GFED), version 3.1 (van der Werf et al.,
2010) for the years 2008 and 2009, and held constant in simulations of future
scenarios. Biogenic emissions originate from the MEGAN database (Guenther et
al., 2012; http://lar.wsu.edu/megan/).

Model evaluation

Overview of the ECLIPSE models and how they were set up for the
years 2008–2009.

Using the ECLIPSE version 4a CLE emissions, simulations were carried out
with a range of models. In addition to the four ESMs used in ECLIPSE
(HadGEM3, ECHAM6-HAM2, NorESM1-M and CESM1/CAM5.2; see Baker et al., 2015a
for descriptions of these models), three CTMs and a Lagrangian particle
dispersion model were used (see Table 2). All models were run for core
periods in 2008 and 2009, when several aircraft campaigns took place in
China and the Arctic, but most models simulated the full 2008–2009 period.
Some models were also run for longer periods and were evaluated together
with other models. For instance, in a comparison against aircraft
measurements, Samset et al. (2014) found that the models systematically
overpredict BC concentrations in the remote troposphere, especially at
higher altitudes. They concluded that the BC lifetime in the models is too
long. A follow-up study suggested that the best match to aircraft
observations could be achieved with strongly increased BC emissions and
decreased lifetimes (Hodnebrog et al., 2014). Daskalakis et al. (2015)
derived changes in the local lifetime of BC up to 150 % associated with
the use of different amounts and spatial distribution of fire emissions in
the same chemistry transport model, demonstrating the dependence of BC
lifetime on its emissions. Tsigaridis et al. (2014) found systematic
underprediction of OA near the surface as well as a large model divergence
in the middle and high troposphere. They attributed these discrepancies to
missing or underestimated OA sources, the removal parameterisations as well
as uncertainties in the temperature-dependent partitioning of secondary OA
in the models. As a consequence of these studies, ECLIPSE models were
improved in terms of emissions (Klimont et al., 2015a, b), secondary OA
formation (Tsigaridis et al., 2014) and removal parameterisations (Samset et
al., 2014; Hodnebrog et al., 2014).

Box and whiskers plots showing the frequency distribution of
measured and modelled CO, NO2, O3 and SO2 mixing ratios or
concentrations representative for background stations in urban and rural
areas in East Asia during August and September 2008 (two left panel columns)
and for rural background stations in Europe for winter (December–February,
DJF) and summer (June–August, JJA) 2008 (two right panel columns). Circles
and central lines show the means and the medians, respectively; box edges
represent the 25th and the 75th percentiles. For East Asia, results are
averaged over several sites: Beijing, Inchon and Seoul for the urban areas,
and Gosan, Kunsan, Kangwha, Mokpo and Taean for rural areas. Results for
individual sites can be found in Quennehen et al. (2015). For Europe, daily
mean observed values are averaged over all stations of the European Monitoring and Evaluation Programme (EMEP) network with
available data. Model data are treated like the observations and only the
days with available observations are taken into account.

The improved ECLIPSE models were evaluated against global data sets such as
aerosol optical depth (AOD), fine-mode AOD and absorption AOD derived from
data of the Aeronet sun photometer network, as well as against various
measurements of aerosol and gas-phase species (Schulz et al., 2015). Here,
we focus on a more detailed regional model evaluation for eastern Asia,
Europe and the Arctic using satellite, airborne and ground-based
measurements of pollutant gases (CO, NO2, O3 and SO2) and
aerosols (Eckhardt et al., 2015; Quennehen et al., 2015). For eastern Asia
in August–September 2008 (Fig. 3, left two columns), data were averaged over
three urban and five rural sites. The models have difficulties reproducing
the urban concentrations, due to their coarse resolution. However,
surprisingly most models overestimated the urban SO2 mixing ratios.
This could be related to power plant emissions that are actually occurring
outside urban boundaries, being placed into the coarse urban model grid
cells. For urban NO2, models deviate less systematically from
observations, with both overestimates and underestimates, and the model mean
captures the observations. For rural NO2, also the individual models
deviate less from the measured concentrations, indicating that the
individual model biases for urban NO2 are very likely mainly due to the
limited model resolution and not to biases in emissions and/or chemical
processes. The measured concentrations of O3 at the rural sites are
matched relatively well (agreement within the range of the temporal
distribution at individual sites) but SO2 is generally overestimated
there as well. The most severe problem at rural sites, however, is a
systematic underestimation of CO mixing ratios, which was attributed to
underestimated CO lifetimes in the models (Quennehen et al., 2015).

A similar comparison was made for Europe with background measurements taken
from stations of the European Monitoring and Evaluation Programme (EMEP)
(Fig. 3, right two columns for winter and summer; see also Schulz et al.,
2015). Overall, over Europe the ECLIPSE model mean captures the mean
observations with the exception during summer for CO that is underestimated
(as in Asia). Summertime O3 is overestimated by many models at rural
locations over Europe and Asia suggesting too much photochemical production
downwind of emission regions.

Satellite-derived AOD measurements were reproduced
quite well by the models over China and Europe (Fig. 4). Evaluation of
individual aerosol components over Asia (Quennehen et al., 2015) shows an
overestimation of the ECLIPSE model-mean surface BC in urban China in summer
2008, which is probably due to the short-term mitigation measures taken
during the Olympic Games. Over Europe, ECLIPSE models satisfactorily
simulate surface BC observations both in winter and summer (Fig. 4).
However, problems were identified over India: Gadhavi et al. (2015) found
that BC concentrations are strongly underestimated in southern India even
when aerosol removal processes in one model were completely switched off in
the region. Furthermore, observed AOD values in northern India are larger
than those simulated by all but two of the ECLIPSE models (Fig. 4). This suggests
that the emissions of BC and precursors of other aerosols are underestimated
for India in the ECLIPSE emission data set. This could be related to the
rapid recent growth of emissions in India (Klimont et al., 2013), which may
be underestimated in the inventories, as well as with problems capturing the
true spatial distribution of emissions in India.

The Arctic was shown previously to be a particularly challenging region for
aerosol model simulations (e.g. Shindell et al., 2008). Aerosol loadings in
the Arctic are generally much lower than in populated regions and the Arctic
encompasses only a small fraction of the Earth. Therefore, impacts of even
large relative errors in the modelled aerosol concentrations in the Arctic
on global radiative forcing and global climate response are relatively
small. Nevertheless, identification of model biases in this remote region is
important as it can lead to improved process understanding, especially of
the aerosol removal mechanisms. An evaluation of the ECLIPSE models over the
Arctic was coordinated with the Arctic Monitoring and Assessment Programme
(AMAP, 2015). Comparisons were made for BC and sulfate for six ground
stations and during six aircraft campaigns (Eckhardt et al., 2015). As an
example, a comparison of the BC concentrations simulated by the ECLIPSE
models with measured equivalent BC is shown in Fig. 5 for the stations
Zeppelin on Svalbard, Pallas in Finland and Tiksi in Siberia. For Zeppelin,
most models clearly underestimate the observed concentrations during winter
and spring, whereas for Pallas which is closer to source regions, the models
tend to overestimate. In general, the model performance (also at other
Arctic sites, not shown) is better than in previous comparisons (e.g.
Shindell et al., 2008). However, very large model underestimates were found
for Tiksi, from where measurement data have only recently become available.
Another ECLIPSE study showed that also the snow BC concentrations are
generally underestimated by models in northern Russia but overestimated
elsewhere in the Arctic (Jiao et al., 2014). It is therefore likely that the
model underestimates are caused by too low BC emissions in Russia in the
ECLIPSE CLE data set. Yttri et al. (2014) attribute this at least partly to
an underestimation of residential wood burning, based on levoglucosan
measurements made at Zeppelin. Eckhardt et al. (2015) suggested that also
SO2 emissions in northern Russia are underestimated. ECLIPSE models
participating in the AMAP (2015) assessment also showed a systematic
underestimation in CO concentrations in the Arctic and a lack of model skill
in simulating reactive nitrogen species important for O3 production.

Monthly (month is displayed on the abscissa) median observed and
modelled BC concentrations for the stations Zeppelin on Svalbard
(11.9∘ E, 78.9∘ N; top), Pallas in Finland
(24.12∘ E, 67.97∘ N; middle) and Tiksi in Siberia
(128.9∘ E, 71.6∘ N; bottom), for late winter–spring
(left column) and summer–autumn (right column) for the years 2008–2009 (for
Tiksi, measured values were available only from July 2009 to June 2010). The
red dashed lines connect the observed median values, the light red shaded
areas span from the 25th to the 75th percentile of the
observations. Modelled median values are shown with lines of different
colour according to the legend. Notice that different concentration scales
are used for individual panels and also for January–May (axis on left hand
side) and June–December (axis on right hand side) periods. Modified from
Eckhardt et al. (2015).

An important finding of the model-measurement comparisons is that overall
the ESMs show a similar performance as the CTMs. This is encouraging for the
further use of the ESMs for determining the climate impacts (Sect. 3.6).
The comparisons led to some further improvements of the ECLIPSE emissions
for version 5, prior to their use for transient climate model simulations.
For instance, wick lamps were identified as an important emission source in
India, the inclusion of which improved the agreement with the observations
in a model sensitivity study (Gadhavi et al., 2015). Other enhancements
(e.g. re-gridding of non-ferrous smelter emissions to improve SO2
emissions in Russia as suggested by Eckhardt et al., 2015) came too late
for the climate impact studies and were only made in version 5a.

Another aspect of model evaluation is to determine the capability of models
to reproduce past trends, and this was tested over Europe. Strong reductions
of aerosol emissions occurred over Europe since the 1980s due to air
quality legislation in western Europe, and since the early 1990s due to
economic restructuring in eastern Europe. This emission reduction is
manifest, for example, in strongly increasing trends in surface solar radiation
(“solar brightening”) and visibility (Stjern et al., 2011), but also in a
stronger warming trend compared to the earlier period in which aerosol
emissions increased (Cherian et al., 2014). The “historical” simulations
contributed to the 5th Coupled Model Intercomparison Project (CMIP5;
Taylor et al., 2012) using previous versions of the ECLIPSE ESMs were
assessed for continental Europe, and compared to observations from the
Global Energy Balance Archive (Gilgen et al., 1998) and the Climatic Research Unit (CRU) of the University of East Anglia (CRU-TS-3.10, Mitchell
and Jones, 2005). The 1960–1980 period shows a strong “solar dimming”
(reduction in surface solar radiation) and small warming, since the
greenhouse-gas-induced warming is offset by the aerosol forcing. The period
1990–2005, in turn, shows the solar brightening, and a much stronger
warming. All three tested models are able to reproduce this strong increase
in warming trend to within their uncertainties (Fig. 6), suggesting that the
climate response to aerosol changes is captured despite the masking
influence of natural climate variability on these trends. However, the
absolute amplitude of the trends is not equally well captured by all models,
indicating that the skill of the ECLIPSE (and other) ESMs to simulate
temperature trends responding to changing aerosol emissions is limited. This
is due to both limitations in the models themselves, the emission input, as
well as the influence of natural climate variability.

Linear trends in (left) surface solar radiation and (right)
near-surface temperature increase per decade over continental Europe from
the “historical” simulations in the CMIP5 archive contributed
by previous versions of three ECLIPSE ESMs. The period 1960–1980 is shown
in red, the period 1990–2005 in blue.

Radiative forcing

To provide input to the metrics needed for designing a mitigation scenario,
dedicated model simulations by three ESMs (ECHAM6-HAM2, HadGEM3, NorESM) and
a CTM (OsloCTM2) were used to establish a matrix of specific RF (Bellouin et
al., 2015) by season and region of emission. Specific RF (SRF) is defined as
the RF per unit change in emission rate once the constituents have reached
equilibrium and is given in mW m-2 (Tg yr-1)-1. To estimate
SRF, the emissions of eight short-lived species (BC, OA, SO2, NH3,
NOx, CO, CH4 and NMVOCs) were reduced by 20 % compared to their
ECLIPSE baseline. These species cause RF themselves and/or lead to the
perturbation of radiative forcers (e.g. O3). The regional reductions
were made for Europe and China, as well as for the global shipping sector
and for a rest-of-the-world region. To account for seasonal differences in
SRF, separate reductions were applied for May–October and November–April.
Henceforth, we will refer to these as Northern Hemisphere (NH) “summer” and
“winter”. Notice that in our case the sign of SRF is opposite to that of
RF, because the imposed emission changes are negative. A reduction of a
warming species gives negative RF values but positive SRF values. It is
important to note that SRF excludes rapid adjustments in the atmosphere,
with the exception that BC semi-direct effects were calculated explicitly,
and stratospheric temperature adjustments were included for O3 and
CH4.

Models generally agreed on the sign of RF and the ranking of the efficiency
of the different emitted species, but disagreed quantitatively (see Bellouin
et al., 2015 for details). The best estimate of a species' RF was considered
to be the average of all models, with the model spread indicating its
uncertainty. However, not all models have calculated RF for all species or
have accounted for all processes. For instance, all models were able to
quantify the aerosol direct effect but only three quantified the first
indirect effect. For BC aerosols only one model quantified the snow albedo
effect and the semi-direct effect explicitly. Therefore, mean RF values were
determined by averaging across all available models for each process
separately. In most cases, all four models were available for this, but for
some processes fewer models had to be used.

The ECLIPSE estimates of specific radiative forcing (SRF;
mWm-2 per Tg yr-1 of emission rate change) for reductions in the
emissions of SO2, NOx, CH4, and BC aerosols in Europe, China
and the global average, separately for NH summer (Sum., May–October) and NH
winter (Win., November–April). Shown are values averaged over all five
models, with the error bars indicating the full range of the model
estimates. Colours indicate the contribution of different forcing
mechanisms. Notice that for CH4 regionality was not accounted for
because, due to its longer lifetime, forcing does not depend on the emission
region.

Figure 7 shows the resulting SRF for reductions in the emissions of
SO2, NOx, CH4 and BC and the processes contributing to the
total forcing, for Europe, China and on global average. The
globally averaged SRF was obtained by adding RF for Europe, China and rest of the world, then normalising to global emission change. The SRF values are
the largest for BC, but note that global emissions of BC are smaller than for the
other species. In addition, the semi-direct effect of BC potentially offsets
a considerable fraction of the aerosol direct RF and RF due to deposition on
snow. However, quantifying the semi-direct effect has large uncertainties because internal variability of the climate system masks tropospheric
adjustments to BC perturbations. This means that the sign of total SRF
exerted by decreases in BC emissions may be negative, if a weak BC direct
effect is more than compensated by a strong semi-direct effect.
Nevertheless, the ECLIPSE BC SRF best estimate of about 50 mW m-2
(Tg[C] yr-1)-1 when semi-direct effects are included is not an
outlier compared to previous estimates, which range from 24 to
108 mW m-2 (Tg[C] yr-1)-1 according to Table 23 of Bond et al. (2013). Moreover, ECLIPSE simulations indicate that the magnitude of the
semi-direct effect is smaller than the direct aerosol effect (Hodnebrog et
al., 2014; Samset and Myhre, 2015), in agreement with most, but not all,
previous studies (see Table 23 of Bond et al., 2013). Reductions in
the emissions of light scattering aerosols such as sulfate stemming from
its precursor SO2 induce a negative SRF. The RF values of aerosols are
generally larger for summer emissions than for winter emissions because of
the stronger insolation. However, there are exceptions to this. For
instance, the BC deposition on snow is more effective for winter emissions
because of the larger snow extent in winter and spring and partial
preservation of deposited BC into spring. Aerosol SRF is also larger in
magnitude for Europe than for China, most likely because of different cloud
regimes which are differently affected by semi-direct and indirect aerosol
effects.

For NOx, SRF is uncertain because decreases in NOx emissions
perturb tropospheric chemistry in two opposite ways, working on different
timescales: first, they reduce tropospheric O3 concentrations, thus
exerting a positive SRF. Second, they increase CH4 concentrations, thus
exerting a negative SRF, with an additional CH4-induced change in
O3. ECLIPSE accounts for those two pathways, and quantifies a third,
whereby reductions in NOx emissions suppress nitrate aerosol formation
and its associated RF. ECLIPSE is therefore able to confirm with confidence
the earlier quantification (Myhre et al., 2013a) that NOx exerts a
negative SRF, because the O3 response is not sufficient to offset the
combined CH4 and nitrate response. For CH4, ECLIPSE finds a
relatively large range of SRF estimates from the models, reflecting the
differences in methane lifetime and methane's effects on ozone and aerosols.

GTP20 values for SLCPs, relative to an equal mass emission of
CO2, for all regions and seasons, decomposed by processes and based on
the RF values shown in Fig. 7. The regions included are Europe (EUR), China
(CHN), global (GLB), and the shipping sector considered separately (SHP),
all for both NH summer (s, May–October) and NH winter (w, November–April).
Uncertainty bars reflect model spread. Only two models calculated effects of
emissions from shipping and there no uncertainty range is given.

The BC radiative forcing is very uncertain and needs some discussion. When
scaled to 100 % BC reductions and reported annually, ECLIPSE BC total RF
and its range are 0.28 (0.02–0.46) W m-2. Neglecting semi-direct
effects, those numbers become 0.41 (0.11–0.48) W m-2. These values are
at the stronger end of the multi-model ACCMIP (Atmospheric Chemistry and
Climate Model Intercomparison Project) estimate of 0.24 ± 0.1 W m-2 (Shindell et al., 2013) and the AeroCom estimate of 0.18 ± 0.07 W m-2
(Myhre et al., 2013b). In contrast, ECLIPSE
estimates are at the middle or weak end of the relatively large estimates
that were reported in several recent assessments: 0.45 (0.30–0.60) W m-2 in UNEP/WMO (2011), 0.71 (0.08–1.27) W m-2
in Bond et al. (2013), and 0.40 (0.08–0.8) W m-2 in Boucher et al. (2013). The
estimates in UNEP/WMO (2011) are skewed towards higher values by accounting
for semi-empirical estimates, which include a brown-carbon contribution, the
possibility that BC semi-direct forcing is the same sign as direct forcing
(even though semi-direct forcing opposes the sign of direct forcing in most
models) and assuming a high efficacy of BC deposition forcing. The large
observationally constrained estimates of BC forcing by Bond et al. (2013),
which influenced the IPCC estimate (Boucher et al., 2013) are due to strong
scaling using Aeronet absorbing AOD measurements. However, these
measurements are highly uncertain and probably biased and the resulting high
BC RF values have recently been called into question by several studies
(Wang et al., 2014; Samset et al., 2014; Wang et al., 2015). There are
several reasons for this. First, the scaled models have substantially
higher BC abundances globally than supported by BC measurements. Wang et al. (2014) and Samset et al. (2014) argue that BC residence time has to be
relatively short to fit observed remote BC concentrations. Furthermore, Wang
et al. (2015) showed that the fairly low resolution of global models induces
an artificial negative bias when comparing to AERONET stations in Asia.
Consequently, in ECLIPSE we have not scaled simulated RF values for BC but
have used native model results.

Climate metrics

ECLIPSE explored various options for climate metrics. In addition to
selecting the most useful metric for designing the mitigation scenario, the
project also made conceptual developments. Collins et al. (2013) developed
further the application of Regional Temperature change Potential (RTP)
presented by Shindell et al. (2012) by accounting for the location of
emissions, thereby opening for regionality for both drivers and responses. Collins et al. (2013) also expanded the GTP concept by tentatively including the
climate-carbon feedback for the non-CO2 gases. So far, this feedback
has only been included for the reference gas CO2 which means that GTPs
(and GWPs) tend to underestimate the relative effect of non-CO2
components (Myhre et al., 2013a). Furthermore, Shine et al. (2015) present a
new metric named the Global Precipitation change Potential (GPP), which is
designed to gauge the effect of emissions on the global water cycle. Of
particular relevance for SLCPs is their demonstration of a strong near-term
effect of CH4 on precipitation change and the role of sustained
emissions of BC and sulfate in suppressing precipitation.

Aamaas et al. (2013) investigated several different metrics and showed that
emissions of CO2 are important regardless of what metric and time
horizon is used, but that the importance of SLCP varies greatly depending on
the metric choices made. MacIntosh et al. (2015) considered the errors made
when calculating RF and climate metrics from multi-model ensembles in
different ways. They showed that the mean metric values are relatively
robust but the estimation of uncertainties is very dependent on the
methodology adopted. Finally, Lund et al. (2014a) applied climate metrics to
quantify the climate impacts of BC and co-emitted SLCPs from on-road diesel
vehicles, and Lund et al. (2014b) considered the special case of a fuel
switch from diesel to biodiesel.

As explained in Sect. 3.5, climate metrics were used in ECLIPSE to
identify specific sets of air pollution reduction measures that result in
net positive climate effects (i.e. reduced warming), considering the
impacts on all co-controlled substances. Based on the RF results shown in
Sect. 3.3, Aamaas et al. (2015) calculated regional and seasonal GTP and
GWP metrics for the SLCP emissions, for various time horizons, and explored
their properties. Of all the explored metrics, the pollution control
analysis was carried out for GWP100 and GTP20, as these two
metrics showed large differences in their quantifications. It was found,
however, that the emerging basket of emission control measures was very
similar for both, although the ranking of the potential climate impacts of
individual measures was sometimes different, with a larger effect of
CH4-related measures for the GWP100 metric (due to the higher value assigned to CH4 by this metric with a longer time horizon).

In the following, we concentrate our analysis on the GTP20 metric,
which is shown in Fig. 8 for a pulse emission relative to an equal mass
emission of CO2 for a selected number of species. The metric builds on
and reflects important aspects of the RF forcing values shown in Fig. 7. For
example, for most species GTP20 values for summer are larger than
values for winter (see, e.g. results for SO2), and values for Europe
are larger than values for Asia. While SO2 only has negative values
(i.e. reduction of SO2 always leads to warming), the opposite is true
for CH4. Figure 8 also shows the contribution from different processes to
the total GTP20 metric, and for BC and NOx they often have a different
sign and their magnitude strongly depends on the emission region and season.
While all of this was accounted for in GAINS (see Sect. 3.5), it is clear
that this makes the choice of mitigation options more uncertain, and this is
further complicated by the fact that BC and NOx emissions are always
associated with co-emissions of other SLCPs (e.g. OA). The GTP20
metric was also determined for all other SLCPs (not shown).

CO2-equivalent emissions (Gt) determined with the GTP20
metric, as a function of time, for global emissions of the current
legislation (CLE) and SLCP mitigation (MIT) scenarios. Lines show the values
for individual forcing components (black CO2, red CH4, blue other
SLCPs), while the shaded areas show the total CO2-equivalent emissions
from all SLCPs and CO2. The blue shading indicates the mitigation
potential of MIT, compared to CLE.

For the implementation of mitigation measures, a pulse emission metric is
not very realistic, as measures are normally introduced gradually, become
more effective with time, and are usually maintained indefinitely.
Therefore, we also considered versions of the GTP and GWP metrics for
sustained emission measures and a linear ramp-up of emission measures over a
15-year time period (Aamaas et al., 2015; see also Boucher and Reddy, 2008).
We chose the ramp-up version of the GTP20 metric for designing our
mitigation scenario with the GAINS model (see Sect. 3.5), as it most
realistically reflects the implementation of mitigation measures. Notice
that the ramp-up version of the GTP20 metric can be derived directly
from its pulse emission version, by linear combination of emission pulses
(see Aamaas et al., 2015, for details). Figure 8 shows the pulse emission
version because pulse emissions are building blocks for various versions of
the metrics, including the ramp-up version.

To go beyond global-mean temperature, it is also possible to calculate
regional surface temperature changes using the RTP concept of Shindell and Faluvegi (2009). This concept maps
RF in one given latitude band to the temperature response in several other
latitude bands and was adopted by Aamaas et al. (2015).
For the mapping, we used the pre-calculated RTP coefficients of Shindell and
Faluvegi (2009). Even though the coefficients are likely model dependent, we
had to use these values because they are not available from any other model
(and specifically not from the ECLIPSE models). The coefficients also seem
fairly robust in comparison with the response to historical aerosol forcing
in several other models (Shindell, 2012). We made two additions, however,
for BC in the Arctic and BC on snow using the method of Lund et al. (2014a)
with results from Samset et al. (2013) and Flanner (2013). Using the RTP
concept and RF calculations from Sect. 3.3 (Bellouin et al., 2015), we
calculated absolute regional temperature change potentials (ARTPs) for the
set of components, regions and seasons for which ECLIPSE has determined RF
values (Fig. 7). Following Collins et al. (2013), the ARTPs were used to
estimate transient surface warming using an impulse response function for
temperature response from Boucher and Reddy (2008) (as was also used for the
GTP calculations). Details of the method and ARTP values will be given in
Aamaas et al. (2015) and results of the ARTP calculations
will be used in Sect. 3.7 for comparisons with transient ESM runs.

Emission mitigation and air quality impacts

For designing a climate-optimised SLCP emission mitigation scenario, the
CO2-equivalent SLCP emissions were minimised using the GAINS model. For
this, the numerical values of the GTP20 metric for the final year 2035
for each species, region and season were implemented into GAINS. The ramp-up
version of this metric assumes a linear implementation of mitigation
measures between the years 2015 and 2030 and thereafter a full
implementation. Mitigation measures typically affect several species at the
same time. For instance, controlling BC emissions leads to a “co-control”
of OA and other species. The GTP20 metric values are different for all
of these species and can be of different sign, and it is important to
determine the net impact of a mitigation measure across all affected
species. In practice, the species-specific metric values were weighted with
the emission factors to obtain the net metric value for each of the
∼ 2000 mitigation measures in every one of the 168 regions
considered in GAINS, and for summer and winter periods separately. These
measures were subsequently ranked according to their CO2-equivalent
total net climate impact as measured by the GTP20 metric, and all
measures with a beneficial climate and air quality impact were included in
the MIT scenario basket (Klimont et al., 2015b); this approach
is consistent with that taken by UNEP/WMO (2011) and Shindell et al. (2012).

The mitigation basket contains three groups of measures: (1) measures that
affect emissions of CH4 that can typically be centrally implemented
(e.g. by large energy companies, municipalities) and which also
impact background O3; (2) technical measures that reduce the emissions
of BC, mainly for small stationary and mobile sources; and (3) non-technical
measures to eliminate BC emissions, e.g. through economic and technical
assistance to the poorest population. While about 220 GAINS model measures
were collected in the mitigation basket, they were merged into
representative measure groups. For example, high emitters are calculated for
each of the GAINS transport subsectors and fuels, while here removing high
emitters is represented by one “measure”. Similarly for cooking stoves,
GAINS estimates mitigation potential for various types of fuels, but all of
these are included further into one category “clean cooking stoves”. Also
for CH4, losses from gas distribution are calculated for several GAINS
end use sectors while here the mitigation potential is reported under one
measure where leaks from low pressure pipelines are reduced. Finally, for
NMVOCs all of the solvent-related options in GAINS (over 50) are categorised
as one measure reducing losses from solvent use activities. Considering the
above, about 50 “measures” represent the about 220 GAINS options that were
included in the mitigation basket. The 17 most effective measures contribute
80 % of the total climate benefit, according to the GTP20 metric,
with CH4 measures contributing about 47 % and BC-focused measures
contributing 33 %. These measures are listed in Table 3. It is interesting
to notice that the top CH4 and BC-focused measures both concern the oil
and gas industry and specifically the venting or flaring of associated gas.

Top-17 mitigation measures contributing together more than 80 %
of the climate benefit. Measures are ranked by importance starting from the
top. Methane measures contribute about 47 % of the benefits according to
the GTP20 metric, while 33 % are attributed to BC-focused measures;
20 % are contributed by measures not listed here.

Relative difference maps (in %) for the surface concentrations
of O3 and PM2.5, as obtained from simulations based on the
emission mitigation (MIT) scenario and the current legislation (CLE)
scenario, i.e. (100 × (MIT - CLE)/CLE). Shown are mean concentration
differences for the period 2041–2050 and averaged over model results from
OsloCTM2, NorESM and HadGEM. The black boxes define the regions used in
Table 4.

Relative differences (%) in O3 and PM2.5 surface
concentrations between simulations using the mitigation (MIT) and current
legislation (CLE) scenarios (given as 100 × (MIT - CLE)/CLE) for
several regions and for the final decade of the simulations (2041–2050). The
regions correspond to the boxes shown in Fig. 10. Except for the
Mediterranean, only the land-based grid cells inside the boxes were used.
Mean values and full model ranges for the three models NorESM, HadGEM and
OsloCTM2 are reported. To exclude effects of changes in sea salt and dust
emissions caused by natural variability and emission responses to forced
changes in meteorological conditions, sea salt and dust concentrations were
kept at CLE levels for the purpose of these calculations.

Loss of statistical life expectancy (months) due to the exposure
to PM2.5 in Europe, for EU-28 and non-EU countries (upper panel) and
for China and India (lower panel). The black bars give the values for the
mitigation (MIT) scenario, whereas the blue increments show the difference
to the current legislation (CLE) scenario. For India and China, green bars
indicate the gains from the implementation of the CLE scenario.

As can be seen in Fig. 2, the mitigation has only minor effects on CO2
emissions, but reduces most SLCPs strongly compared to the CLE scenario. By
2030, CH4 emissions are reduced by about 50 % and BC emissions by
nearly 80 %. OA is co-controlled with BC, causing a nearly 70 % (not
shown) reduction of its emissions, as for some sectors BC outweighs the
cooling effects of OA. While NOx emission reductions are in most cases also
not preferred by the GTP20 metric (see Fig. 8), reductions stem from
the co-control when higher Euro standards are introduced; they reduce
significantly several pollutants such as BC, CO, NMVOC and also NOx
(Klimont et al., 2015a, b). By contrast, SO2 emissions are nearly the
same in both the CLE and MIT scenarios, as for the key sectors emitting
SO2, the warming by SO2 reductions (see Fig. 8) cannot be
outweighed by co-control of species whose reduction would lead to cooling.
Thus, SO2 reductions are largely avoided.

The global CO2-equivalent emissions (calculated using the GTP20
metric values) are shown as a function of time in Fig. 9. Values are shown
for CLE and MIT and are split into contributions from CH4 and other
SLCP emissions. For comparison, CO2 emissions are also shown. On the
short timescale of the GTP20 metric, CH4 and CO2 emissions
are nearly equally important in the CLE scenario. The CO2-equivalent
emissions of CH4 are, however, reduced by 50 % in MIT. The
CO2-equivalent emissions for the other SLCPs are negative in both
scenarios, indicating that in total they have a cooling impact. As the
mitigation reduces preferentially warming components, this cooling becomes
even stronger in the MIT case. The total CO2-equivalent emissions
(including CO2 emissions) are reduced substantially in the MIT scenario
(blue shaded area in Fig. 9 shows the reduction), for example by about
70 % in the year 2030. About 56 % of this reduction is due to CH4
measures and ∼ 44 % is due to other measures. It is
important to notice that the effect of the SLCP mitigation is relatively
large for the rather short time horizon of the GTP20 metric; it would
be smaller for a longer time horizon. Similarly, the relative importance of
CH4 compared to other SLCP emissions would be increased for a longer
time horizon.

Surface concentrations of SLCPs resulting from the CLE and MIT scenarios
were determined from the various model simulations. Figure 10 shows maps of
the model-mean relative differences for O3 and PM2.5 for the last
decade (2041–2050) of the transient simulations and Table 4 reports
differences for several regions shown with boxes in Fig. 10. Concentrations
of O3 (Fig. 10, upper panel) are reduced globally, with reductions of
more than 12 % in most of the Northern Hemisphere and the strongest
reductions of up to about 20 % occurring in East Asia. For instance, in
eastern China (see Table 4), O3 is reduced by 19.3 % (16.0–24.4 %).
BC and OA concentrations (not shown) were also globally reduced, with BC
reductions reaching more than 80 %. For sulfate (not shown), the relative
changes are much smaller than for BC and OA and both increases and decreases
occur – a consequence of the relatively small global SO2 emission
reductions (see Fig. 2). Changes in PM2.5 concentrations (Fig. 10,
lower panel) are smaller because of large contributions from natural sources
(e.g. sea salt, dust, wildfire emissions). PM2.5 concentrations in the
SLCP source regions were reduced by typically 10–20 % and up to nearly
50 % in smaller regions. Reductions are strongest in Asia, for instance
19.8 % (17.9–22.5 %) in India (Table 4).

In summary, air pollutant concentrations in the MIT scenario are
dramatically reduced compared to the CLE scenario, especially in the
polluted (and heavily populated) source regions. This indicates the
beneficial effect of the SLCP mitigation on air quality. Nevertheless, a
word of caution is needed for O3. The O3 concentrations increase
strongly (typically between 5 and 20 %, depending on region and season)
between now and 2050 in the CLE scenario, because of increasing CH4 and
NOx emissions. Therefore, global-mean O3 concentrations even in
the MIT scenario do not decrease substantially with time. However, strong
relative O3 reductions by the mitigation are simulated in the SLCP
source regions (Table 4), which more than outweigh the overall concentration
increase in the CLE scenario. Therefore, population exposure to O3
decreases with time in the MIT scenario.

For Europe and Asia, the GAINS model also contains source–receptor
relationships which allow for the estimation of the impacts of emissions on
human health. GAINS quantifies the impacts of changes in SLCP emissions on
the long-term population exposure to PM2.5 in Europe, China and India
and estimates the resulting premature mortality, in terms of reduced
statistical life expectancy and cases of premature deaths (Amann et al.,
2011). Calculations follow the recommendations of the findings of the WHO
review on health impacts of air pollution and recent analyses conducted for
the Global Burden of Disease project (Lim et al., 2012), relying on the
results of the American Cancer Society cohort study (Pope III et al., 2002) and
its re-analysis (Pope III et al., 2009). It uses cohort- and country-specific
mortality data extracted from life table statistics to calculate for each
cohort the baseline survival function over time. Notice, however, that, in
contrast to the changes of pollutant concentrations presented in Fig. 10,
the quantification of human health impacts is based on results from a single
model.

Using GAINS, we estimate that in the EU the loss of statistical life
expectancy will be reduced from 7.5 months in 2010 to 5.2 months in 2030 in
the CLE scenario. The ECLIPSE mitigation measures (MIT) would reduce
statistical life shortening by another 0.9 months (Fig. 11, upper panel),
resulting in 4.3 months of reduction in life expectancy. This value is only
slightly above the target of 4.1 months that has been set by the European
Commission in its 2013 Clean Air Policy proposal (EC, 2013). Population in
non-EU countries would gain approximately 1 month life expectancy from the
implementation of the ECLIPSE measures in 2030 (Fig. 11, upper panel).

In China and India, the potential health gains from the implementation of
the ECLIPSE measures are significantly larger, however, starting from a
substantially higher level of life shortening due to PM2.5 (Fig. 11,
lower panel). In China, the ECLIPSE measures would in the year 2030 extend
the life expectancy of the population by approximately 1.8 months and reduce
the premature deaths attributable to PM2.5 by 150 000–200 000 cases
per year.

In India, rapid increase in energy consumption, together with lacking
regulations on emission controls for important sources (e.g. power
generation) and poor enforcement of existing laws (e.g. for vehicle
pollution controls) will lead to a steep increase in PM2.5 levels. If
no saturation of health impacts is assumed for such high levels (there are
no cohort studies available for such high concentrations), with conservative
assumptions GAINS estimates approximately 850 000 cases annually of
premature deaths from air pollution in 2010. For 2030, PM2.5 exposure
would increase by more than 50 %, and at the same time the population
would increase and age. Combined, these factors would let the premature
deaths from air pollution grow by approximately 125 % to 1.9 million cases
in 2030, with another doubling to 3.7 million cases in 2050. Against this
background, the ECLIPSE measures would avoid more than 400 000 cases of
premature deaths in 2030 and almost 700 000 in 2050. Using the loss in
statistical life expectancy as an alternative metric, the ECLIPSE measures would gain 11–12 months in life expectancy for the Indian population (Fig. 11, lower panel).

Climate impacts

The climate impacts of SLCPs were determined with four ESMs (HadGEM3,
NorESM, ECHAM6-HAM2/MPIOM, and CESM–CAM4) in two different experiments. In
the first experiment, still part of the outer loop of the spiral in Fig. 1,
all land-based anthropogenic emissions of each of SO2, OA and BC were
removed one at a time and the models were run to equilibrium; in the other experiment,
the mitigation scenario described in Sect. 3.5 was followed in a series of
transient ensemble model runs, constituting the inner spiral loop in Fig. 1. Note
that all ESMs implicitly include the semi-direct effect, while for the
radiative forcing calculations this was calculated explicitly by only one
model.

For the first experiment, the four ESMs, with full ocean coupling were run
for a control simulation and a perturbation run for 50 years, after a
spin-up period to equilibrium. The control simulation used ECLIPSE V4a
emissions for the year 2008 (except for CESM–CAM4, which used year 2000
emissions). For the perturbation, 100 % of the land-based emissions of the
three individual species were removed in turn to achieve discernible climate
responses. While only three ESMs ran the experiments for SO2 and OA,
all four ESMs ran the BC experiment and three of these used two or three
ensemble members each. Only NorESM included the effect of albedo reduction
by BC deposited on snow and ice. This experiment is described in detail in
Baker et al. (2015a), where more detailed descriptions of the models used
can also be found. Here, we provide a synthesis of the results.

Global-mean annual average changes in (a) surface temperature and
(b) precipitation averaged over all land areas excluding ice sheets, from
the four ESMs (one was run only for BC), for a complete removal of all
land-based emissions of a particular species, compared to the ECLIPSE
version 4 baseline emissions for the year 2008 (CESM–CAM4 used the year
2000). The plot shows results averaged over 50-year model simulations; for
HadGEM and NorESM (CESM–CAM4), two (three) ensemble members each were run
for BC (named e1, e2, e3 in the labels). Error bars are 95 % confidence
intervals in the mean, based on 50 annual means from each model and thus
only reflect the uncertainty of the mean caused by natural variability
within each model.

When removing SO2 emissions, all three models show an increase of
global-mean surface temperature (Fig. 12a) by 0.69 K (0.40–0.84 K) on
average. Here, the first value is the multi-model mean, whereas the values
in brackets give the full range of results obtained with the individual
models. We will keep this notation throughout the rest of the paper, unless
otherwise noted. The zonal-mean temperature change is positive at all
latitudes and increases with latitude in the Northern Hemisphere, reaching
2.46 K (1.38–3.31 K) at the North Pole (Fig. 13a). It is also positive for
most regions of the Earth and is more positive over the continents than over
the oceans (Baker et al., 2015a). The models also agree that removing
SO2 emissions results in an increase in global-mean precipitation (Fig. 12b), which is in line with the expected impact from a global temperature
increase. The precipitation increases are particularly strong over India and
China because of a northward shift of the Intertropical Convergence Zone
(ITCZ; see Fig. 13b), which is in accordance with previous studies (e.g.
Broccoli et al., 2006).

Annual mean changes in zonal-mean surface temperature (left
panels a, c, e) and precipitation (right panels b, d, f) for a complete
removal of all land-based emissions of (a–b) SO2, (c–d) BC, and (e–f)
OA. For BC, some models ran two or three ensemble members (e1, e2, e3).
Notice the differences in scales between different panels.

Time evolution of differences in global-mean surface temperature
between transient simulations following the mitigation (MIT) and the current
legislation (CLE) scenario, i.e. (MIT–CLE), for the four ECLIPSE models.
Negative values mean that temperatures are lower in the MIT than in the CLE
scenario. The ensemble means for each model are shown as thick lines,
whereas the individual ensemble members are shown with thin lines.

The response to removing anthropogenic BC emissions, -0.05 K (-0.15 to
+0.08 K), is much smaller than the SO2 response and the models do not
all agree on the sign of the global-mean response. The multi-model mean is
slightly negative (Fig. 12a) but within ±0.5 K everywhere on the
globe (not shown) and the zonal-mean temperature response differs from model
to model (Fig. 13c). The NorESM model shows the strongest cooling in the
Arctic, likely because it is the only model accounting for snow albedo
changes. Precipitation changes from removing BC emissions are also small,
but consistently positive in all models (Fig. 12b) despite the cooling in
most models. This is consistent with calculations of the relationship
between atmospheric RF and precipitation change in Andrews et al. (2010) and
Kvalevåg et al. (2013) (see also Shine et al., 2015). The multi-model
temperature response to removing OA emissions is similar to that for
removing SO2, but much weaker overall (Figs. 12a and 13e).

In summary, the emission perturbation studies show that elimination of
anthropogenic SO2 emissions leads to robust warming, elimination of OA
also leads to – albeit much weaker – warming, whereas elimination of BC
leads to a small temperature response with substantial differences between
the models but slight cooling in the multi-model mean. This could be due to
the different sizes of the indirect and semi-direct effects of BC in
different models, shown in ECLIPSE (Hodnebrog et al., 2014), and possibly to
unforced responses of climate system components, especially sea ice, that
happen to counteract the small temperature response.

Seasonal and annual mean differences in surface temperatures (in
K) in various regions (a–f) and for the whole globe (g) between transient
simulations of the mitigation (MIT) and the current legislation (CLE)
scenario, i.e. (MIT–CLE), averaged over the last 10 years of the simulations
(2041–2050). Regions are defined as (a) 45–65∘ N, 10∘ W–65∘ E, (b) 30–45∘ N, 10∘ W–65∘ E, (c)
7–35∘ N, 68∘ E–90∘ E,
(d) 24–48∘ N, 80–132∘ E, (e) 60–90∘ N, 180∘ W–180∘ E, (f) 30–60∘ N, 120–50∘ W.
Results are shown for the four ECLIPSE models individually and for the
multi-model mean. Negative values mean that temperatures are lower in the
MIT than in the CLE scenario. Error bars on the model-mean values show the
standard deviations of the individual model results.

Annual average differences in (top) surface temperature and
(bottom) precipitation over Europe between the transient simulations based
on the mitigation (MIT) and the current legislation (CLE) scenario, averaged
over the last 10 years of the simulations (2041–2050). Stippling shows where
all four models agree on the sign of the response. The thick horizontal line
distinguishes the southern and northern Europe boxes. In the top panel,
negative values mean that temperature in the MIT scenario is lower than in
the CLE scenario; in the bottom panel, positive values mean that there is
more precipitation in the MIT than in the CLE scenario.

The second ESM experiment simulated the transient responses to the climate-optimised
SLCP mitigation of Sect. 3.5 (Baker et al., 2015b). For this, the four
ESMs ran three ensemble members each for both the CLE and MIT scenario until
the year 2050. The CLE scenario resulted in an increase of global-mean
surface temperature of 0.70 ± 0.14 K (the value following the “±” sign gives the standard deviation obtained from all ensemble members)
between the years 2006 and 2050 in the multi-model ensemble mean. A large
part of this increase is a response to increasing CO2 concentrations,
and consequently the MIT scenario also showed a (albeit smaller)
temperature increase. As we here are mainly interested in the response to
the SLCP mitigation, we only consider the difference between the MIT and the
CLE scenario in the following. Time series of the global-mean temperature
difference between the two scenarios are shown in Fig. 14. There is a
considerable spread between individual ensemble members even from the same
model. This reflects simulated natural climate variability superimposed on
the response to the SLCP mitigation, which makes diagnosis of the latter
difficult. Systematically negative global-mean temperature responses emerge
only when the mitigation measures are fully implemented. The multi-model
mean global temperature (MIT–CLE) difference is -0.22 ± 0.07 K for the
final 10 years, confirming that the mitigation could be successful in
reducing the warming of the CLE scenario.

The relative cooling is particularly strong over the continents and the weakest
over the northern North Atlantic (not shown), similar to the response
patterns seen in the perturbation simulations. Figure 15 shows mean
temperature responses for the last decade of the simulation, for various
regions. The strongest relative cooling between MIT and CLE of about 0.44 K
(0.39–0.49 K) are found for the Arctic, with peak values of about 0.62 K
(0.37–0.84 K) occurring in autumn (winter values are similar). Over Europe,
differences are more consistent between models and more strongly negative in
the southern parts than in the northern parts (see also Fig. 16). This can
be explained by the larger natural climate variability in northern Europe.
Mainly small and inconsistent results are found over India, due to model
differences in the shift of the ITCZ, whereas changes over China and North
America are consistently negative from (almost) all models and for all
seasons.

The precipitation responses are less robust which, in the tropics, is due to
model differences in the migration of the ITCZ. Nevertheless, there are
regions with consistent responses of all models. Of particular interest is
the precipitation increase over southern Europe (Fig. 16). Seen against the
background of expected warming and drying in the Mediterranean area due to
CO2-driven climate change, the precipitation increase due to the SLCP
mitigation would be beneficial, especially given the fact that it is
strongest from spring to autumn (Fig. 17), with 15 (6–21) mm yr-1 increase
(corresponding to more than 4 % (2–6 %) of total precipitation) and
combined with a temperature reduction that is also the largest during that
period (Fig. 15). Thus, our mitigation approach would help to alleviate
drought and water shortages in the Mediterranean area in summer, as they are
expected for the future (Orlowsky and Seneviratne, 2012).

Seasonal and annual mean differences in precipitation (in mm yr-1) over northern (a) and southern (b) Europe between transient
simulations of the mitigation (MIT) scenario and the current legislation
(CLE) scenario, averaged over the last 10 years of the simulations
(2041–2050). Regions are defined as in Fig. 15a, b. Results are shown
for the four ECLIPSE models individually and for the multi-model mean.
Positive values mean that MIT simulations have more precipitation than CLE
simulations. Error bars on the model-mean values show the standard
deviations of the individual model results.

Time evolution of differences in global-mean surface temperature
between transient simulations following the mitigation (MIT) and the current
legislation (CLE) scenario (i.e. MIT–CLE), as simulated by the CESM–CAM4
model with a slab-ocean representation. One experiment (red lines) included
all emission reductions of the MIT scenario, whereas another experiment
(blue lines) included only the CH4 emission reductions of the MIT
scenario. Ensemble mean results are shown with thick lines, individual
ensemble members with thin lines.

Time evolution of difference in global-mean surface temperature
for the MIT and CLE scenario calculated with the ARTP-based method. Solid
lines are the total difference (MIT–CLE), while the dashed lines give the
responses to CH4 mitigation only. The solid lines can be compared
directly with the results from the transient model simulations shown in
Fig. 14.

In the aerosol perturbation experiments, we have seen that SO2 emission
reductions lead to strong warming and OA reductions to weaker warming,
whereas BC emission reductions lead to much weaker and model-dependent
cooling. While the GTP20 metric attributes ∼ 44 % of
the reduction in CO2-equivalent emissions to BC-related measures, it
was interesting to clarify whether this is reflected in the transient
climate model simulations, and how this fraction changes with time. For
this, two additional experiments with four ensemble members each were run
with one ESM (CESM–CAM4) with a slab-ocean representation (for greater
computational efficiency): one with all emission reductions (MIT), and one
with only CH4 emission reductions (MIT–CH4-only). Ozone reductions
resulting from CH4 emission measures are included in the
MIT–CH4-only simulations, whereas O3 changes from non-CH4
measures are omitted. The two experiments yielded a relatively similar
temporal characteristic of global-mean temperature difference (see Fig. 18),
where the MIT scenario led to 0.45 ± 0.04 K less warming than CLE, and
the MIT–CH4-only scenario led to 0.41 ± 0.04 K less warming than
CLE for the final decade of the simulation (2041–2050). The difference
between the two scenarios is not significant, owing to the small number of
ensemble members. Thus, the simulations indicate a dominant (∼ 90 %) role of CH4 emission reductions and a small (∼ 10 %) but perhaps non-negligible role of non-CH4 mitigation measures
for reducing the warming by 2041–2050.

Notice that the warming reduction for the MIT scenario with the slab-ocean
version of the CESM–CAM4 model is twice as strong as with the more realistic
full-ocean version shown in Fig. 14. However, given the results with the
slab-ocean version, a dominant role of CH4 in the mitigation is likely
also for the full-ocean simulations. In fact, this adds to the explanation of
why the temperature response in Fig. 14 emerges from natural variability
only about 10 years (i.e. approximately the lifetime of CH4) after the
start of the mitigation measures.

We have seen in Sect. 3.5 that the chosen set of mitigation measures also
leads to considerable reductions in PM and O3 and thus improves air
quality. If implemented as such, the mitigation package as a whole would
therefore have beneficial impacts on both air quality and climate. However,
the experiments conducted here also suggest that the co-benefits for climate
and air quality result mainly from CH4 mitigation, which improves air
quality (via clear reductions of the background surface O3
concentrations) and reduces warming considerably. The co-benefits of the
non-CH4 SLCP mitigation measures, on the other hand, are quite limited.
These measures improve air quality strongly (via reductions of PM
concentrations and O3) but reduce warming only slightly. This does not of
course mean that all of the individual measures have small co-benefits.
For instance, it is likely that mitigating sources with the highest BC/OA
ratio would lead to larger co-benefits. Partly, the small co-benefits for
the non-CH4 SLCP measures are a result of the design of our MIT
scenario, which was based on the GTP20 metric and this seems to suggest
a stronger warming reduction due to non-CH4 SLCP measures than seen in
the transient ESM simulation results. The consistency between the two
approaches will be discussed in the next section, but it is clear that if
the metric was too “optimistic” with respect to the achievable warming
reductions by non-CH4 SLCP mitigation measures, some individual
measures will have been included in the MIT basket, which in the transient
simulations might have actually led to warming enhancement instead of
warming reduction.

Closing the loop: Climate impacts from metric calculations and transient
model simulations, and applications of the metrics

The purpose of this section is to compare the climate impacts of the SLCP
mitigation as estimated with the metrics and as obtained from the transient
simulations, as well as to show applications of the metrics. A perfect
agreement between the two methods cannot be expected, as the metrics assume
linearity of the climate response to all individual forcing contributions
and are also valid for a specific time horizon. Furthermore, after using the
RF values calculated in ECLIPSE, the methodology adopted to calculate the
GTP values uses a representation of both the size and time dependence of the
response derived from one particular (pre-ECLIPSE) ESM calculation. Since
the different ESMs used in ECLIPSE have a diverse range of climate
sensitivities and different representations of uptake of heat by the oceans,
this is a further important reason why exact agreement should not be
expected. The metrics are also calculated for a specific climate and are
less accurate for periods with a changed climate. One example is the BC snow
albedo effect, which is gradually reduced over time because the snow and ice
extent decreases as the climate warms. On the other hand, diagnosis of
temperature responses from the transient climate simulations is also
associated with large error bars because of natural climate variability.
Nevertheless, a comparison of the two methods is an important consistency
check but, to our knowledge, has never been done before for a SLCP
mitigation scenario and using an ensemble of ESMs.

The ARTP metric described in Sect. 3.5, applied over a series of
timescales, allows for calculation of the regional (in broad latitude bands)
temperature response from the reduction of individual species in the
mitigation basket as a function of time. Based on these regional responses a
total global-mean response is derived which can be compared directly with
the global-mean temperature difference between the MIT and CLE transient
simulations in the ESMs.

The ARTPs depend on the global climate sensitivity assumed for the impulse
response function used (Shindell et al., 2012). To provide a consistent
comparison with the ESM transient simulations, the ARTPs have been scaled by
the individual climate sensitivities for each ESM. Furthermore, not all the
ESMs include all components and forcing mechanisms in their transient
simulations, e.g. only NorESM includes the forcing due to BC deposited on
snow and ice. To make a consistent comparison, ARTP contributions were
summed only over the components and processes included in each ESM.
Figure 19 shows the global-mean temperature responses using the ARTP method,
which can be compared directly with the temperature changes obtained by the
transient ESM simulations shown in Fig. 14. The solid lines in Fig. 19 show
the total global temperature response of the mitigation, while the dashed
lines show the contribution from CH4 reductions only. For the last
decade of the simulation (2041–2050) both the ESMs and ARTPs give a mean
global response of -0.22 K. The ranges of the estimates based on the four
models are also very similar (-0.15 to -0.29 K for the ESMs, and -0.13 to
-0.33 K for the ARTPs). Note, however, that the factors contributing to
these ranges are not the same in these two estimates. For the ESMs, the
differences in radiative forcing, the models' climate sensitivities and
internal variability determine the range, while for the ARTP-based estimate
the differences in which processes causing RF are included in each model and
the model's climate sensitivities are accounted for. The ranking of the
responses between the individual models are, however, not identical.
Nevertheless, based on these results we conclude that for the global-mean
response to SLCP mitigation the ARTP-based method simulates well the full
ESM simulations to estimate the impact of SLCP mitigation.

The ARTP-based method suggests a larger contribution of non-CH4 SLCPs
to the temperature response for the 2041–2050 decade (∼ 22 %; see dashed lines in Fig. 19) than the transient simulations using
the slab-ocean version of the CESM–CAM4 model discussed in the previous
section (∼ 10 %). One reason for this disagreement is that
many of the BC-related measures included in the mitigation basket were
relatively OA rich and this makes their net temperature response extremely
uncertain, especially when aerosol indirect effects and the semi-direct
effect are considered. An even larger contribution (44 %) of non-CH4
SLCPs to the CO2-equivalent emission reductions of the MIT scenario was
obtained with the GTP20 metric directly (see Sect. 3.5).
However, this value is valid only for the temperature response in the year
2035, for which the relative contribution of the non-CH4 SLCPs (which
are shorter-lived than CH4) is larger than for later years. As the
diagnostic uncertainties for a single year are very large for the transient
ESM simulations, we abstain from comparisons. Direct comparisons are anyway
not meaningful, since the metrics include all recognised RF mechanisms,
whereas most ESMs lack some of these (e.g. snow albedo changes by BC).

To further investigate the ability of a simplified metrics method to
represent the regional response simulated by the ESMs, we use the ARTPs to
estimate the response in four broad latitude bands and these results can be
compared to the corresponding results from the ESMs. Figure 20 shows the
20-year mean results (2031–2050) for both methods. The general pattern of
the responses with largest impact for the main source region at Northern Hemisphere mid-latitudes (NHML) and in the Arctic is well captured by the
ARTP method. However, as expected the agreement between the estimates is not
as good as for the global mean (correlation coefficient of 0.68 for the 16
data points). The ARTP-based estimates are close to the ESM means for the
NHML and the tropics, while for the Arctic and for the Southern Hemisphere (SH) the ARTP method
underestimates the response simulated by the ESMs. The reason for the more
pronounced Arctic amplification in our ESMs than for the ARTPs is probably a
less pronounced Arctic amplification in the model of Shindell and Faluvegi (2009), from where the RTP-coefficients were taken. For future development
and use of the ARTP method, RTP-coefficients are needed also from other
ESMs.

Temperature changes (MIT–CLE) in four latitude bands (Arctic,
Northern Hemisphere Mid-Latitudes (NHML), tropics and Southern Hemisphere)
calculated with the ESMs and ARTP-based method. Mean changes over the
2031–2050 period are shown for individual models and the mean over the
models. Notice the different temperature scales.

The motivation for establishing these ARTPs is that after quality control
they provide policy makers with a relatively simple tool to quantify how
sectorial emissions (i.e. by region, sector and component) contribute to
temperature change over time in broad latitude bands. Figure 21 illustrates
this potential for the MIT scenario. It shows the responses (mean of the
ARTP-based estimates for the four ESMs) for mitigation taking place in
Europe, China and globally. The larger relative impact on the Arctic by
mitigation of European emissions compared to mitigation in China is clearly
seen, while the impact for NHML is about three times larger in absolute
terms for mitigation of SLCPs in China. These results can then easily be
further analysed by separating the impact by emitted species (Fig. 21, panel
d), or by separating the impact of a single emission sector. Figure 22, for
instance, shows the estimated impact on Arctic temperatures by global
mitigation of SLCPs from the residential heating and cooking sector, broken
down to contributions from individual SLCP species. Notice that it would be
prohibitively expensive to run an ensemble of ESM simulations that is large
enough to detect the small temperature response resulting from such minute
emission changes.

Total Arctic surface temperature change (K) by global mitigation
of residential burning (heating and cooking) in the ECLIPSE emission
scenario (MIT–CLE), as obtained with the ARTP method.

Conclusions

ECLIPSE has come to a number of important scientific conclusions, which are
also of high relevance for climate and air quality policy:

ECLIPSE has created a new inventory for anthropogenic SLCP emissions, including scenarios for the future.
An important finding is the large range of possible future developments of anthropogenic SLCP emissions, which
even for a single future energy pathway substantially exceeds the range of SLCP emissions given in IPCC's RCPs.
The large range results from the uncertainties of future air quality policies, as well as from the expected
level of implementation and enforcement of existing policies (Klimont et al., 2015a, b).

Detailed comparisons between measured and modelled distributions of aerosol, O3 and other SLCP gases
have shown that for many substances the models are in good agreement with available background observations. The
model performance of the ESMs is similar to that of CTMs. For BC, in particular, the agreement between models
and measurements has improved for the Arctic (Eckhardt et al., 2015), which is partly the result of accounting
for emissions from gas flaring and emission seasonality (Stohl et al., 2013). Outside the Arctic, a reduction of
the BC lifetime led to improvements (Samset et al., 2014). Nevertheless, our comparisons suggest underestimates
of BC and aerosol precursor emissions in high latitude Russia and in India. Furthermore, it was found that SO2
concentrations are overestimated and CO concentrations are underestimated by the models (Quennehen et al., 2015)
at the surface in Asia and Europe during summer and autumn. The CO underestimate is likely associated with a too
short CO lifetime in the models. Ozone, on the other hand, is generally overestimated at rural locations. Such
discrepancies may affect model responses to emission perturbations and thus radiative forcing.

Earth system models can reproduce the accelerated upward trend of surface temperature over Europe that was observed
when aerosol precursor emissions were reduced in the 1990s (leading to solar brightening), after a period of emission
growth in the 1960s–1980s (leading to solar dimming) (Cherian et al., 2014).

ECLIPSE performed detailed multi-model calculations of RF for all considered SLCP species, as a function of emission
region and season. It is found that the absolute values of specific RF for aerosols are generally larger in summer than
in winter (Bellouin et al., 2015). It is found that the semi-direct effect on clouds, although highly uncertain, can
potentially offset a considerable fraction of the direct positive RF of BC. This, together with reduced BC lifetimes,
causes the net RF for BC calculated in ECLIPSE to be only weakly positive, which is different from most previous studies.

NOx emissions affect the concentrations of O3, CH4 and nitrate aerosols. The first effect leads to positive
RF, while the latter two cause negative RF. We have quantified all these effects and can state with confidence that the current
net RF of global historical NOx emissions is negative. The forward looking metrics GWP and GTP for NOx are negative
as well, except for short time horizons.

ECLIPSE had a focus on calculation and testing of emission metrics, which led to a better understanding of existing metrics
(Aamaas et al., 2013), further development of the applications of the RTP concept (Collins et al., 2013) and introduction of new
metric concepts such as the Global Precipitation change Potential (GPP) by Shine et al. (2015). After careful consideration of
the alternatives, we chose a 15-year ramp-up version (i.e. assuming a linear implementation of measures) of the GTP20
metric for designing a SLCP mitigation scenario.

The GTP20 metric was implemented into the GAINS model to identify mitigation measures (MIT) that have beneficial impacts
on both air quality and climate. We find that the 17 most important mitigation measures would contribute more than 80 % of the
climate benefits according to the GTP20 metric. The top measures both for CH4 and BC mitigation are to prevent the
venting (for CH4) and flaring (for BC) of gas associated with the oil production. For CH4, measures on shale gas
production, waste management and coal mines were also important. For non-CH4 SLCPs, elimination of high-emitting vehicles
and wick lamps, as well as reducing emissions from coal and biomass stoves, agricultural waste, solvents and diesel engines were also important.

Full implementation of these measures (the MIT scenario) would reduce global anthropogenic emissions of CH4 and BC by
50 and 80 %, respectively. As a result of co-control with BC, emissions of organic aerosols would also be reduced by 70 %,
whereas emissions of CO2 and SO2 would hardly be changed. Based on the GTP20 metric, the CO2-equivalent
emissions (including CO2 emissions) would be decreased by about 70 % in the year 2030, with about 56 % of the decrease
caused by CH4 measures and 44 % caused by non-CH4 SLCP measures.

The mitigation scenario would reduce surface concentrations of O3 and PM2.5 globally compared to the CLE scenario,
with BC reductions of more than 80 % in some areas. We estimate that in the EU the loss of statistical life expectancy due to
air pollution will be reduced from 7.5 months in 2010 to 5.2 months in 2030 in the CLE scenario. The MIT measures would gain
another 0.9 months. Substantially, larger health improvements from SLCP measures are estimated for China (1.8 months) and India (11–12 months).

Climate impacts of the MIT scenario were investigated with ESM ensemble transient simulations of both the CLE and MIT scenario
(Baker et al., 2015b). Multi-model ensemble mean global-mean surface temperature in the CLE scenario increased by 0.70 ± 0.14 K
between the years 2006 and 2050. The ensemble mean global-mean surface warming for the last decade of the simulation (2041–2050)
was, however, 0.22 ± 0.07 K weaker for the MIT scenario, demonstrating the effect of the SLCP mitigation. The response was
strongest in the Arctic, with warming reduced by about 0.44 K (0.39–0.49 K).

In addition to global annual mean temperature change, there are other climate parameters that are of relevance for policy
decisions (e.g. changes in precipitation, regional temperatures). The SLCP reductions in the MIT scenario led to particularly
beneficial climate responses in southern Europe, where the surface warming was reduced by about 0.3 K from spring to autumn and
precipitation rates were increased by about 15 (6–21) mm yr-1 (15 mm yr-1 corresponding to more than 4 % of total precipitation),
compared to the CLE scenario. Thus, the mitigation could help to alleviate expected future drought and water shortages in the Mediterranean region.

Additional ESM transient simulations, which only included the CH4 emission reductions, led to a global warming reduction
that amounted to about 90 % of the reduction produced by the simulations using the full set of measures for the final decade of
the simulations (2041–2050). This suggests that, for longer time horizons, the net climate benefits from our chosen non-CH4 SLCP
mitigation measures in terms of global annual mean temperature change are limited, probably due to small forcing and co-emitted
cooling species. Nevertheless, if implemented as such, the mitigation package as a whole would have beneficial impacts on both air
quality and climate, and for the latter, also in other climate variables than global annual mean temperature change such as
regional changes in temperature and precipitation.

For the first time, ECLIPSE compared the temperature response to an SLCP mitigation scenario as it is given by climate metrics
(using the ARTP method) and as it is simulated with transient ESM simulations. This is crucial for the application of metrics, which
– because of their simplicity and flexibility – are very relevant in a policy context where they can substitute full ESM simulations
which are expensive and impractical for small perturbations. Both approaches give a global mean reduced warming of the surface
temperatures by 0.22 K (and similar uncertainty ranges) for the period 2041–2050. Also the large-scale pattern of the response
(with strongest warming reductions in the Arctic) is reproduced similarly by both methods, even though the agreement is
not as
good as the global mean.

The metrics-based approach and the transient model simulations agree less on the relative contribution of CH4 and
non-CH4 SLCP mitigation measures to the reduced warming. While the metrics-based approach suggests that the non-CH4 measures
account for 22 % of the global-mean temperature response for 2041–2050, the transient simulations result in a contribution from
non-CH4 measures of only about 10 %. One reason for this disagreement is that many of the BC-related measures included in
the mitigation basket were relatively OA rich and this makes their net temperature response more uncertain, especially when aerosol
indirect effects are considered. Furthermore, small cooling influences are easily masked by unforced variability in fully coupled
climate simulations.

The major share of the cooling effect in our SLCP mitigation scenario is contributed by CH4 reductions, with 20–30 % of
the difference in near-term global-mean climate warming from the reduction of non-CH4 SLCPs (multi-model range 0.01–0.06 K for
time periods from 2021–2040, according to metrics-based estimates). Thus, to maximise climate co-benefits of non-CH4 SLCPs, sources
with the highest BC / OA emission ratios should be addressed with priority. At the same time, air pollution policies should consider
mitigation of CH4, with clear co-benefits for climate warming and air quality via reduced surface O3 concentrations.

The ECLIPSE mitigation scenario has been developed to be representative of a mitigation strategy that considers both climate and
air quality, assuring reduced climate forcing without detrimental impact on air quality; as a matter of fact, strong air quality
co-benefits were identified. Real-world scenarios are likely to favour particular policy objectives and will also consider the costs
for the mitigation measures. More work is therefore needed to explore a larger range of scenarios. By demonstrating the efficiency
and capacity of the metrics-based approach to quantify temperature changes and its consistency with transient climate model simulations,
ECLIPSE has opened the way to explore a large number of such scenarios. This would be an impossible task if transient climate
ensemble model simulations were needed for each.

The number of models contributing to the ECLIPSE project was relatively small. While the models were shown to be largely
representative of results obtained from larger model ensembles, this makes quantification of mean values and especially uncertainties
(e.g. of RF or temperature response) dependent on the particular properties of the ECLIPSE models. For a more comprehensive quantification
of uncertainties, it is therefore recommended to repeat the modelling exercises presented in this paper with a larger international model ensemble.

Acknowledgements

The research leading to these results has received funding from the European
Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no.
282688 – ECLIPSE. Some partners are involved in the Nordic Centre of Excellence eSTICC (eScience Tools for Investigating Climate Change), funded by Nordforsk. We thank D. Shindell and an anonymous referee for their
comments, which helped to improve this paper.Edited by: Y. Balkanski